{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uJHywE_oL3j2"
      },
      "source": [
        "# ResNet on CIFAR10 with Flax and Optax.\n",
        "\n",
        "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.sandbox.google.com/github/google-deepmind/optax/blob/main/examples/cifar10_resnet.ipynb)\n",
        "\n",
        "This notebook trains a residual network (ResNet) with optax on CIFAR10 or CIFAR100."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 10233,
          "status": "ok",
          "timestamp": 1701459080832,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "VaYIiCnjL3j3",
        "outputId": "1d3a996a-de0a-4644-d1e2-c55b38b7af51"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "JAX running on GPU\n"
          ]
        }
      ],
      "source": [
        "import functools\n",
        "from collections.abc import Callable\n",
        "from typing import Any, Sequence, Tuple, Optional, Dict\n",
        "\n",
        "from flax import linen as nn\n",
        "\n",
        "import jax\n",
        "import jax.numpy as jnp\n",
        "\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "import optax\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "from functools import partial\n",
        "\n",
        "# hide the GPU from tensorflow, otherwise it might\n",
        "# reserve memory on it\n",
        "tf.config.experimental.set_visible_devices([], \"GPU\")\n",
        "\n",
        "# Show on which platform JAX is running.\n",
        "print(\"JAX running on\", jax.devices()[0].platform.upper())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jve2h810L3j3"
      },
      "outputs": [],
      "source": [
        "# @markdown Total number of epochs to train for:\n",
        "MAX_EPOCHS = 50  # @param{type:\"integer\"}\n",
        "# @markdown Number of samples in each batch:\n",
        "BATCH_SIZE = 128  # @param{type:\"integer\"}\n",
        "# @markdown The initial learning rate for the optimizer:\n",
        "PEAK_LR = 0.12  # @param{type:\"number\"}\n",
        "# @markdown The model architecture for the neural network. Can be one of `'resnet1'`, `'resnet18'`, `'resnet34'`, `'resnet50'`, `'resnet101'`, `'resnet152'`, `'resnet200'`:\n",
        "MODEL = \"resnet18\"  # @param{type:\"string\"}\n",
        "# @markdown The dataset to use. Could be either `'cifar10'` or `'cifar100'`:\n",
        "DATASET = \"cifar10\"  # @param{type:\"string\"}\n",
        "# @markdown The amount of L2 regularization (aka weight decay) to use:\n",
        "L2_REG = 1e-4  # @param{type:\"number\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iLGeV4y4DBkL"
      },
      "source": [
        "CIFAR10 and CIFAR100 are composed of 32x32 images with 3 channels (RGB). We'll now load the dataset using `tensorflow_datasets` and display a few of the first samples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "executionInfo": {
          "elapsed": 60564,
          "status": "ok",
          "timestamp": 1701459141394,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "zynvtk4wDBkL",
        "outputId": "3aad151e-127f-4cc9-9965-5b445d90e6d0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading and preparing dataset 162.17 MiB (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9fc3ac24a084a06bfac06aca8a0b124",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Completed...: 0 url [00:00, ? url/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e4afe4d7aee44e5b878be7aa32dd230",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Dl Size...: 0 MiB [00:00, ? MiB/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2dc0c9031d9d487b982ccd56ab0a5232",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Extraction completed...: 0 file [00:00, ? file/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ffb1c2e6e3df4bd3935ffbb06c9f2238",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating splits...:   0%|          | 0/2 [00:00\u003c?, ? splits/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae3b5631f7a64ba5bf0e01f8de8a4dbe",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train examples...:   0%|          | 0/50000 [00:00\u003c?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ac91a663d15a4af588c81e5b5558fad0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incompleteOTLR4P/cifar10-train.tfrecord*...:   0%|          …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05baa1e32c5046ddb0b51ed741446992",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating test examples...:   0%|          | 0/10000 [00:00\u003c?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ec8fc53b35a4912838201a231053364",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Shuffling /root/tensorflow_datasets/cifar10/3.0.2.incompleteOTLR4P/cifar10-test.tfrecord*...:   0%|          |…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 20 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "(train_loader, test_loader), info = tfds.load(\n",
        "    DATASET, split=[\"train\", \"test\"], as_supervised=True, with_info=True\n",
        ")\n",
        "NUM_CLASSES = info.features[\"label\"].num_classes\n",
        "IMG_SIZE = info.features[\"image\"].shape\n",
        "\n",
        "\n",
        "def plot_sample_images(loader):\n",
        "  loader_iter = iter(loader)\n",
        "  _, axes = plt.subplots(nrows=4, ncols=5, figsize=(6, 4))\n",
        "  for i in range(4):\n",
        "    for j in range(5):\n",
        "      image, label = next(loader_iter)\n",
        "      axes[i, j].imshow(image, cmap=plt.cm.gray_r, interpolation=\"nearest\")\n",
        "      axes[i, j].set_axis_off()\n",
        "      axes[i, j].set_title(\n",
        "          info.features[\"label\"].names[label], fontsize=10, y=0.9\n",
        "      )\n",
        "\n",
        "\n",
        "plot_sample_images(train_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XlSCdO8yDBkM"
      },
      "source": [
        "The accuracy of the model can be improved significantly through data augmentation. That is, instead of training on the above images, we'll generate random modifications of the images and train on those. This is done by using the `transform` argument of `tfds.load` to apply a random crop, random horizontal flip, and random color jittering.\n",
        "\n",
        "In the next cell we apply these transformations on the above images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "executionInfo": {
          "elapsed": 1072,
          "status": "ok",
          "timestamp": 1701459142465,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "dVkDlShoDBkM",
        "outputId": "8595c20c-e476-4851-d78f-d07a4cc7c3fc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 600x400 with 20 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def augment(image, label):\n",
        "  \"\"\"Performs data augmentation.\"\"\"\n",
        "  image = tf.image.resize_with_crop_or_pad(image, 40, 40)\n",
        "  image = tf.image.random_crop(image, [32, 32, 3])\n",
        "  image = tf.image.random_flip_left_right(image)\n",
        "  image = tf.image.random_brightness(image, max_delta=0.2)\n",
        "  image = tf.image.random_contrast(image, 0.8, 1.2)\n",
        "  image = tf.image.random_saturation(image, 0.8, 1.2)\n",
        "  return image, label\n",
        "\n",
        "\n",
        "train_loader_augmented = train_loader.map(augment)\n",
        "plot_sample_images(train_loader_augmented)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITnYDuiIDBkM"
      },
      "source": [
        "We now shuffle the data in the train set and create batches of size `'BATCH_SIZE'` for both train and test set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TSjMCbukDBkN"
      },
      "outputs": [],
      "source": [
        "train_loader_batched = train_loader_augmented.shuffle(\n",
        "    buffer_size=10_000, reshuffle_each_iteration=True\n",
        ").batch(BATCH_SIZE, drop_remainder=True)\n",
        "\n",
        "test_loader_batched = test_loader.batch(BATCH_SIZE, drop_remainder=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrSSsvpUDBkN"
      },
      "source": [
        "Now that the data is ready, let's define the model. We will be implementing ResNet from scratch using Flax. More examples on how to use Flax can be found [here](https://github.com/google/flax/tree/main/examples).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PNKfhWNrnLSP"
      },
      "outputs": [],
      "source": [
        "ModuleDef = Any\n",
        "\n",
        "\n",
        "class ResNetBlock(nn.Module):\n",
        "  \"\"\"ResNet block.\"\"\"\n",
        "\n",
        "  filters: int\n",
        "  conv: ModuleDef\n",
        "  norm: ModuleDef\n",
        "  act: Callable\n",
        "  strides: Tuple[int, int] = (1, 1)\n",
        "\n",
        "  @nn.compact\n",
        "  def __call__(\n",
        "      self,\n",
        "      x,\n",
        "  ):\n",
        "    residual = x\n",
        "    y = self.conv(self.filters, (3, 3), self.strides)(x)\n",
        "    y = self.norm()(y)\n",
        "    y = self.act(y)\n",
        "    y = self.conv(self.filters, (3, 3))(y)\n",
        "    y = self.norm(scale_init=nn.initializers.zeros_init())(y)\n",
        "\n",
        "    if residual.shape != y.shape:\n",
        "      residual = self.conv(self.filters, (1, 1), self.strides, name=\"conv_proj\")(\n",
        "          residual\n",
        "      )\n",
        "      residual = self.norm(name=\"norm_proj\")(residual)\n",
        "\n",
        "    return self.act(residual + y)\n",
        "\n",
        "\n",
        "class BottleneckResNetBlock(nn.Module):\n",
        "  \"\"\"Bottleneck ResNet block.\"\"\"\n",
        "\n",
        "  filters: int\n",
        "  conv: ModuleDef\n",
        "  norm: ModuleDef\n",
        "  act: Callable\n",
        "  strides: Tuple[int, int] = (1, 1)\n",
        "\n",
        "  @nn.compact\n",
        "  def __call__(self, x):\n",
        "    residual = x\n",
        "    y = self.conv(self.filters, (1, 1))(x)\n",
        "    y = self.norm()(y)\n",
        "    y = self.act(y)\n",
        "    y = self.conv(self.filters, (3, 3), self.strides)(y)\n",
        "    y = self.norm()(y)\n",
        "    y = self.act(y)\n",
        "    y = self.conv(self.filters * 4, (1, 1))(y)\n",
        "    y = self.norm(scale_init=nn.initializers.zeros_init())(y)\n",
        "\n",
        "    if residual.shape != y.shape:\n",
        "      residual = self.conv(self.filters * 4, (1, 1), self.strides, name=\"conv_proj\")(\n",
        "          residual\n",
        "      )\n",
        "      residual = self.norm(name=\"norm_proj\")(residual)\n",
        "\n",
        "    return self.act(residual + y)\n",
        "\n",
        "\n",
        "class ResNet(nn.Module):\n",
        "  \"\"\"ResNetV1.\"\"\"\n",
        "\n",
        "  stage_sizes: Sequence[int]\n",
        "  block_cls: ModuleDef\n",
        "  num_classes: int\n",
        "  num_filters: int = 64\n",
        "  dtype: Any = jnp.float32\n",
        "  act: Callable = nn.relu\n",
        "  conv: ModuleDef = nn.Conv\n",
        "  initial_conv_config: Optional[Dict[str, Any]] = None\n",
        "\n",
        "  @nn.compact\n",
        "  def __call__(self, x, train: bool = True):\n",
        "    conv = partial(self.conv, use_bias=False, dtype=self.dtype)\n",
        "    norm = partial(\n",
        "        nn.BatchNorm,\n",
        "        use_running_average=not train,\n",
        "        momentum=0.9,\n",
        "        epsilon=1e-5,\n",
        "        dtype=self.dtype,\n",
        "    )\n",
        "\n",
        "    initial_conv_config = dict(self.initial_conv_config)\n",
        "    initial_conv_config.setdefault(\"kernel_size\", 7)\n",
        "    initial_conv_config.setdefault(\"strides\", 2)\n",
        "    initial_conv_config.setdefault(\"with_bias\", False)\n",
        "    initial_conv_config.setdefault(\"padding\", \"SAME\")\n",
        "    initial_conv_config.setdefault(\"name\", \"initial_conv\")\n",
        "\n",
        "    x = conv(self.num_filters, **self.initial_conv_config)(x)\n",
        "    x = norm(name=\"bn_init\")(x)\n",
        "    x = nn.relu(x)\n",
        "    x = nn.max_pool(x, (3, 3), strides=(2, 2), padding=\"SAME\")\n",
        "    for i, block_size in enumerate(self.stage_sizes):\n",
        "      for j in range(block_size):\n",
        "        strides = (2, 2) if i \u003e 0 and j == 0 else (1, 1)\n",
        "        x = self.block_cls(\n",
        "            self.num_filters * 2**i,\n",
        "            strides=strides,\n",
        "            conv=conv,\n",
        "            norm=norm,\n",
        "            act=self.act,\n",
        "        )(x)\n",
        "    x = jnp.mean(x, axis=(1, 2))\n",
        "    x = nn.Dense(self.num_classes, dtype=self.dtype)(x)\n",
        "    x = jnp.asarray(x, self.dtype)\n",
        "    return x\n",
        "\n",
        "\n",
        "ResNet1 = partial(ResNet, stage_sizes=[1], block_cls=ResNetBlock)\n",
        "ResNet18 = partial(ResNet, stage_sizes=[2, 2, 2, 2], block_cls=ResNetBlock)\n",
        "ResNet34 = partial(ResNet, stage_sizes=[3, 4, 6, 3], block_cls=ResNetBlock)\n",
        "ResNet50 = partial(ResNet, stage_sizes=[3, 4, 6, 3], block_cls=BottleneckResNetBlock)\n",
        "ResNet101 = partial(ResNet, stage_sizes=[3, 4, 23, 3], block_cls=BottleneckResNetBlock)\n",
        "ResNet152 = partial(ResNet, stage_sizes=[3, 8, 36, 3], block_cls=BottleneckResNetBlock)\n",
        "ResNet200 = partial(ResNet, stage_sizes=[3, 24, 36, 3], block_cls=BottleneckResNetBlock)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eokfjycN6Bh5"
      },
      "source": [
        "Note that we're overwriting some of the default parameters in this implementation, such as the `kernel_size` and `strides` of the convolutions. The default values of (7, 7) and 2 respectively are too large for the small 32x32 images in this dataset, so we reduce these parameters to (3, 3) and 1 respectively."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_kbXJT07L3j5"
      },
      "outputs": [],
      "source": [
        "initial_conv_config = {\"kernel_size\": (3, 3), \"strides\": 1, \"padding\": \"SAME\"}\n",
        "\n",
        "RESNET_CONSTRUCTOR = {\n",
        "    \"resnet1\": ResNet1,\n",
        "    \"resnet18\": ResNet18,\n",
        "    \"resnet34\": ResNet34,\n",
        "    \"resnet50\": ResNet50,\n",
        "    \"resnet101\": ResNet101,\n",
        "    \"resnet152\": ResNet152,\n",
        "    \"resnet200\": ResNet200,\n",
        "}\n",
        "\n",
        "net = RESNET_CONSTRUCTOR[MODEL](num_classes=NUM_CLASSES, initial_conv_config=initial_conv_config)\n",
        "\n",
        "def predict(params, bn_params, inputs, is_training=False):\n",
        "  all_params = {\"params\": params, \"batch_stats\": bn_params}\n",
        "\n",
        "  def train_fn(inputs):\n",
        "    logits, net_state = net.apply(\n",
        "        all_params, inputs, train=True, mutable=[\"batch_stats\"]\n",
        "    )\n",
        "    return logits, net_state\n",
        "\n",
        "  def eval_fn(inputs):\n",
        "    logits = net.apply(all_params, inputs, train=False, mutable=False)\n",
        "    return logits, {\"batch_stats\": bn_params}\n",
        "\n",
        "  return jax.lax.cond(\n",
        "      is_training, lambda x: train_fn(x), lambda x: eval_fn(x), inputs\n",
        "  )\n",
        "\n",
        "\n",
        "@partial(jax.jit, static_argnums=(3,))\n",
        "def loss_accuracy(params, bn_params, data, is_training: bool = True):\n",
        "  \"\"\"Computes loss and accuracy over a mini-batch.\n",
        "\n",
        "  Args:\n",
        "    params: parameters of the model.\n",
        "    bn_params: state of the model.\n",
        "    data: tuple of (inputs, labels).\n",
        "    is_training: if true, uses train mode, otherwise uses eval mode.\n",
        "\n",
        "  Returns:\n",
        "    loss: float\n",
        "    aux: dictionary with keys \"accuracy\" and \"batch_stats\".\n",
        "  \"\"\"\n",
        "  inputs, labels = data\n",
        "  logits, net_state = predict(params, bn_params, inputs, is_training=is_training)\n",
        "  mean_loss = optax.softmax_cross_entropy_with_integer_labels(\n",
        "      logits=logits, labels=labels\n",
        "  ).mean()\n",
        "  accuracy = jnp.mean(jnp.argmax(logits, axis=-1) == labels)\n",
        "  l2_params = jax.tree.leaves(params)\n",
        "  # Computes regularization on all except batchnorm parameters.\n",
        "  weight_l2 = sum(jnp.sum(x**2) for x in l2_params if x.ndim \u003e 1)\n",
        "  loss = mean_loss + 0.5 * L2_REG * weight_l2\n",
        "  return loss, {\"accuracy\": accuracy, \"batch_stats\": net_state[\"batch_stats\"]}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "executionInfo": {
          "elapsed": 358,
          "status": "ok",
          "timestamp": 1701459142819,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "WWIy3HjZd4nz",
        "outputId": "1ba98f00-aae5-431e-b7b3-0b79c7963272"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 640x480 with 1 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "iter_per_epoch_train = info.splits[\"train\"].num_examples // BATCH_SIZE\n",
        "lr_schedule = optax.linear_onecycle_schedule(\n",
        "    MAX_EPOCHS * iter_per_epoch_train, PEAK_LR\n",
        ")\n",
        "\n",
        "iterate_subsample = np.linspace(0, MAX_EPOCHS * iter_per_epoch_train, 100)\n",
        "plt.plot(\n",
        "    np.linspace(0, MAX_EPOCHS, len(iterate_subsample)),\n",
        "    [lr_schedule(i) for i in iterate_subsample],\n",
        "    lw=3,\n",
        ")\n",
        "plt.title(\"Learning rate\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Learning rate\")\n",
        "plt.grid()\n",
        "plt.xlim((0, MAX_EPOCHS))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_2VpAIid4nz"
      },
      "source": [
        "In the next two cells initialize variables and states. We also define a convenience function `dataset_stats` that we'll call once per epoch to collect the loss and accuracy of our solver over the test set."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xLTQpLg1L3j5"
      },
      "outputs": [],
      "source": [
        "solver = optax.sgd(lr_schedule, momentum=0.9, nesterov=False)\n",
        "\n",
        "# Initializes parameters.\n",
        "rng = jax.random.PRNGKey(0)\n",
        "dummy_data = jnp.ones((1,) + IMG_SIZE, dtype=jnp.float32)\n",
        "dummy_targets = jnp.ones(1, int)\n",
        "variables = net.init({\"params\": rng}, dummy_data)\n",
        "\n",
        "var_params, net_state = variables[\"params\"], variables[\"batch_stats\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fu9Lu_dC0u3I"
      },
      "outputs": [],
      "source": [
        "# Defines parameter update function.\n",
        "var_solver_state = solver.init(var_params)\n",
        "\n",
        "\n",
        "def dataset_stats(params, net_state, data_loader):\n",
        "  \"\"\"Computes loss and accuracy over the dataset `data_loader`.\"\"\"\n",
        "  all_accuracy = []\n",
        "  all_loss = []\n",
        "  for cur_batch in data_loader.as_numpy_iterator():\n",
        "    batch_loss, batch_aux = loss_accuracy(\n",
        "        params, net_state, cur_batch, is_training=False\n",
        "    )\n",
        "    all_loss.append(batch_loss)\n",
        "    all_accuracy.append(batch_aux[\"accuracy\"])\n",
        "  return {\"loss\": np.mean(all_loss), \"accuracy\": np.mean(all_accuracy)}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49L9gffyDBkO"
      },
      "source": [
        "Finally, we do the actual training. The next cell performs `'MAX_EPOCHS'` epochs of training. Within each epoch we iterate over the batched loader `train_loader_batched`, and once per epoch we also compute the test set accuracy and loss."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 69602,
          "status": "ok",
          "timestamp": 1701460033377,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "loz4M9I5SLRu",
        "outputId": "b2aae413-5568-4e83-f070-3ec5b85982a7"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch:  0\n",
            "Test set accuracy:  0.3795072\n",
            "Train set accuracy:  0.27672276\n",
            "Epoch:  10\n",
            "Test set accuracy:  0.76382214\n",
            "Train set accuracy:  0.7876202\n",
            "Epoch:  20\n",
            "Test set accuracy:  0.84395033\n",
            "Train set accuracy:  0.87233573\n",
            "Epoch:  30\n",
            "Test set accuracy:  0.8411458\n",
            "Train set accuracy:  0.9211739\n",
            "Epoch:  40\n",
            "Test set accuracy:  0.9015425\n",
            "Train set accuracy:  0.9701923\n"
          ]
        }
      ],
      "source": [
        "train_accuracy = []\n",
        "train_losses = []\n",
        "\n",
        "# Computes test set accuracy at initialization.\n",
        "test_stats = dataset_stats(var_params, net_state, test_loader_batched)\n",
        "test_accuracy = [test_stats[\"accuracy\"]]\n",
        "test_losses = [test_stats[\"loss\"]]\n",
        "\n",
        "\n",
        "@jax.jit\n",
        "def train_step(params, net_state, solver_state, batch):\n",
        "  # Performs a one step update.\n",
        "  (loss, aux), grad = jax.value_and_grad(loss_accuracy, has_aux=True)(\n",
        "      params, net_state, batch, is_training=True\n",
        "  )\n",
        "  updates, solver_state = solver.update(grad, solver_state)\n",
        "  params = optax.apply_updates(params, updates)\n",
        "  return params, solver_state, loss, aux\n",
        "\n",
        "\n",
        "# Executes a training loop.\n",
        "for epoch in range(MAX_EPOCHS):\n",
        "  train_accuracy_epoch = []\n",
        "  train_losses_epoch = []\n",
        "\n",
        "  for train_batch in train_loader_batched.as_numpy_iterator():\n",
        "    var_params, var_solver_state, train_loss, train_aux = train_step(\n",
        "        var_params, net_state, var_solver_state, train_batch\n",
        "    )\n",
        "    net_state = train_aux[\"batch_stats\"]\n",
        "    train_accuracy_epoch.append(train_aux[\"accuracy\"])\n",
        "    train_losses_epoch.append(train_loss)\n",
        "\n",
        "  #  Once per epoch, makes a pass over the test set to compute accuracy.\n",
        "  test_stats = dataset_stats(var_params, net_state, test_loader_batched)\n",
        "  test_accuracy.append(test_stats[\"accuracy\"])\n",
        "  test_losses.append(test_stats[\"loss\"])\n",
        "  train_accuracy.append(np.mean(train_accuracy_epoch))\n",
        "  train_losses.append(np.mean(train_losses_epoch))\n",
        "\n",
        "  # Prints accuracy every 10 epochs.\n",
        "  if epoch % 10 == 0:\n",
        "    print(\"Epoch: \", epoch)\n",
        "    print(\"Test set accuracy: \", test_accuracy[-1])\n",
        "    print(\"Train set accuracy: \", np.mean(train_accuracy_epoch))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 669
        },
        "executionInfo": {
          "elapsed": 414,
          "status": "ok",
          "timestamp": 1701460033792,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "dy2EnSiCL3j5",
        "outputId": "abb277d5-f971-4385-94b7-80131ec45b8d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "\u003cFigure size 1600x600 with 2 Axes\u003e"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))\n",
        "\n",
        "plt.suptitle(f\"{MODEL} on {DATASET}\", fontsize=20)\n",
        "\n",
        "ax1.plot(\n",
        "    test_accuracy,\n",
        "    lw=3,\n",
        "    marker=\"s\",\n",
        "    markevery=5,\n",
        "    markersize=10,\n",
        "    label=\"test set\",\n",
        ")\n",
        "ax1.plot(\n",
        "    train_accuracy,\n",
        "    lw=3,\n",
        "    marker=\"^\",\n",
        "    markevery=5,\n",
        "    markersize=10,\n",
        "    label=\"train set (stochastic estimate)\",\n",
        ")\n",
        "ax1.set_ylabel(\"Accuracy\", fontsize=20)\n",
        "ax1.grid()\n",
        "ax1.set_xlabel(\"Epochs\", fontsize=20)\n",
        "ax1.set_xlim((0, MAX_EPOCHS))\n",
        "ax1.set_ylim((0, 1))\n",
        "\n",
        "ax2.plot(\n",
        "    test_losses, lw=3, marker=\"s\", markevery=5, markersize=10, label=\"test set\"\n",
        ")\n",
        "ax2.plot(\n",
        "    train_losses,\n",
        "    lw=3,\n",
        "    marker=\"^\",\n",
        "    markevery=5,\n",
        "    markersize=10,\n",
        "    label=\"train set (stochastic estimate)\",\n",
        ")\n",
        "ax2.set_ylabel(\"Loss\", fontsize=20)\n",
        "ax2.grid()\n",
        "ax2.set_xlabel(\"Epochs\", fontsize=20)\n",
        "ax2.set_xlim((0, MAX_EPOCHS))\n",
        "\n",
        "# set legend at the bottom of the plot\n",
        "ax1.legend(\n",
        "    frameon=False, fontsize=20, ncol=2, loc=2, bbox_to_anchor=(0.3, -0.1)\n",
        ")\n",
        "\n",
        "ax2.set_yscale(\"log\")\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "executionInfo": {
          "elapsed": 3,
          "status": "ok",
          "timestamp": 1701460033792,
          "user": {
            "displayName": "Masha Samsikova",
            "userId": "12506121178718715127"
          },
          "user_tz": 0
        },
        "id": "pKxQZ13kL3j5",
        "outputId": "2b4ababf-18cf-41e0-be5c-419ea8e19a63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final accuracy on test set:  0.91366184\n"
          ]
        }
      ],
      "source": [
        "# Finally, let's print the test set accuracy\n",
        "print(\"Final accuracy on test set: \", test_accuracy[-1])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dpg7oiGEQy22"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [
        {
          "file_id": "1xKCiYlOcLL9j5zvQLKZobMq5P0hNwIFS",
          "timestamp": 1701463035990
        }
      ]
    },
    "jupytext": {
      "formats": "ipynb,md:myst"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.11"
    },
    "vscode": {
      "interpreter": {
        "hash": "52d10dda8dd4acbc0136d307f056abb067e83eda640decd801853dd83bdd7356"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
